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The 6 Stages In The Evolution of AI and Customer Experience

8 out of 10 businesses have either already implemented AI (37%) or are planning to adopt it by 2020 (41%). They understand that they must enable AI-powered experiences to better serve customers and to keep up with competitors. But even with adoption and interest being as high as it is, we’re just at the beginning of the AI journey. In this article, we take a look at how the 6 evolutionary stages of AI are significantly shaping new customer experience expectations. What are some of the game changers?

Customers want your business to use Artificial Intelligence (AI) to improve their experience and make their life easier – even if they don’t know what it is or what it does.

8 out of 10 businesses have either already implemented AI (37%) or are planning to adopt it by 2020 (41%). They understand that they must enable AI-powered experiences to better serve customers and to keep up with competitors.

But even with adoption and interest being as high as it is, we’re just at the beginning of the AI journey.

In this article, we take a look at how the 6 evolutionary stages of AI are significantly shaping new customer experience expectations:

Stage 1: Curation

When you type anything into Google, you’re met by a barrage of search results. I just typed “Artificial Intelligence” into the search engine and was met by a grand total of 330 millionresults.

Talk about information overload, right?

Not quite.

Studies show that less than 10% of us read past Google’s first page of results, which means that we aren’t actually overloaded with information. We’ve put our trust in Google’s algorithm and conditioned our brains to get what we need from the top 3-5 search results on the first page, or we refine our search.

This wasn’t always the case. When the internet was young, the likes of Google and AOL tried to curate the right content for the right search query, but they often missed the target.

However, Google has become exceedingly good at giving us what we want by matching the best content to each query – and AI has helped.

What does this mean for the customer experience?

Fewer frustrating experiences, as well as the feeling of serendipity, discovery, and enjoyment while shopping.

Digitally native retailers are already setting new standards by capturing and using data to create highly curated experiences through the use of AI.

Instead of exhausted customers wasting their time clicking through pages of products they have no interest in, they are able to discover unique, interesting products that match their tastes.

Curated shopping is one of the firm’s most important projects for the next 12 to 18 months, says Zalando CEO Rubin Ritter.

The German fashion juggernaut offers 300,000 new products each year, potentially overwhelming the company’s 22 million active customers. But by analyzing both shopping and search trends, Zalando learns a lot about a customer’s preferences and is able to curate their experience.

If a user’s browsing behavior indicates that they’re into sports, Zalando will provide relevant imagery to inspire them. If a user shows interest in dark colors, they’ll be taken straight to the those types of products.

Stage 2: Customized Information

Picture it: It’s 7 am and time for work, but you’re anxious about being late. It happened yesterday by 15 minutes and you were given a warning by your boss.

It wasn’t your fault – the traffic was awful!

What do you do to avoid allowing it to happen again?

In 2018, you can turn to an AI-powered app on your smartphone to give you the lowdown on the fastest way to work.

Just like the Waze traffic app. It uses AI to determine the best route for your commute and uses machine learning to learn your usual driving patterns and when the traffic on your usual routes is unusually heavy. The recent “Where to park” feature also provides users the opportunity to see any available parking lots nearby.

What does this mean for the customer experience?

Better customer service and more useful answers and solutions.

With AI machine learning and natural language processing, businesses are able to understand customer queries and intent so that they can respond more accurately and in real-time.

For example, when a customer asks, “when will my order arrive?”, a voice-enabled assistant or chatbot will not take them to an FAQ page with generic delivery times, but will instead provide customized information without any delay.

One of the largest credit card issuers in China, China Merchants Bank, has implemented an AI bot to deal with issues such as payments and credit card balances.

The bot quickly gets customers the information they need. At 1.5 to 2 million conversations daily, it handles an inquiry volume that would typically require thousands of additional employees to answer.

Stage 3: Recommendations

Recommendations are essential for eCommerce stores. They are proven to boost conversions and increase the number of cross- and upsells.

And it’s clear why: people would be overloaded by choice without support to find and choose the right products and services. Using AI to make recommendations is the top scenario users feel comfortable with, no matter the industry.

Amazon was one of the first companies to provide recommendations based on purchase history and viewed items – and do it well. 35% of its revenue is generated by their recommendation engine.

The company’s competitive advantage is led by AI. Their new machine-learning infrastructure drives the product recommendations system, helping it be smarter in suggesting what to read next, what items to add to a shopping list, and what movie to watch tonight.

What does this mean for the customer experience?

Better recommendations based on actual needs, just like from a friend.

While the quality of recommendations has improved over the years, it’s still lacking since it only uses implicit data such as purchase history or viewed products.

For more intelligent and relevant product recommendations, you must combine implicit data with explicit data. Explicit data is shared by the customer and helps businesses understand their real needs, brands they love, colors, styles and more.

However, gathering this data is difficult from a UI and user experience standpoint. You can’t get it by asking users to fill out forms. This would feel more like filling out your life history at the hospital and would not make for a positive experience.

The solution is to engage customers in a conversation just as a store owner or shopkeeper would.

AI and machine learning is now capable of having these meaningful conversations with customers. Businesses can learn which questions to ask when and in which order to deliver conversational experiences and truly personalized recommendations.

The solution engages users in a natural conversation to find out about their hair type, length and goals. It then analyzes the customer’s responses, identifies suitable products, and makes more relevant recommendations based on their needs.

Given the flexibility of the technology, businesses can implement these AI-driven dialogues not only in web-interfaces, but also chatbots and voice-enabled assistants.

Stage 4: Predictions

Today, predictive analytics is less common than the above three stages. Companies have been using it for several years to varying degrees of success. While it’s more difficult to nail, it’s at the heart of what AI is and does.

Take, for example, a self-driving car. For this technology to be a success, it needs to be able to predict what a good driver would do in a specific situation.

Predictive analytics is informing a number of sectors. Debt collection apps allow the collector to target debtors more likely to pay faster. We also see predictions at work in inventory management apps, where AI is used to make more accurate forecasts.

FutureMargin, for example, is a Shopify App that uses AI to help businesses optimize prices, profit, and inventory by a variety of factors, including predicting demand and seasonal variations in products.

What does this mean for the customer experience?

Proactive, hyper-personalized and extremely relevant interactions.

Predictive analytics not only helps businesses increase profits and improve margins, it also lets them understand how to engage with customers and increase loyalty in more relevant and personalized ways throughout different touchpoints in the customer lifecycle.

The use cases are plentiful; these are only some of the ways predictive analytics helps create exceptional customer experiences like never before:

Marketing: deliver the right message at the right time

Sales: identify and target high-value customers

Service: predict user behavior to deliver proactive customer support

Stage 5: Automation

The next stage in the evolution of AI is automation.

Many of our daily tasks are already being automated. AI will likely reach a point where even shopping for your basic necessities will become entirely automated.

That said, there is yet no specific time frame for when we will reach the advanced stage of full automation.

What does this mean for the customer experience?

In an automated world, on the basis of predictive analytics and sensor data, restocking becomes completely autonomous.

An often-cited example to help people envision the future is a smart fridge.

It knows what type of milk you drink (almond milk? cow milk? soy milk? rice milk?), keeps track of the amount you use every day, and predicts when you’ll have run out so that it can place an order right in time. Self-driving vehicles will prepare your order and a drone will deliver the milk to your front door.

Never again will there be a Sunday without milk for your morning coffee.

Sounds futuristic? It does. But the building blocks are already in place to make this happen.

The jury is still out whether customers want to give up this much control. New survey results from the Integer Group show that a majority of respondents would like Alexa to find great deals on regular purchases, remind them when they need to restock and create shopping lists for them.

But when it comes to actually placing an order everyday items, only 1 in 5 would be comfortable letting AI do their shopping for them.

Stage 6: Contextual Analysis

The final phase for AI is contextual analysis. Like automation, it is still some way off into the future, but once it’s here it will be a game changer.

This is how it will work:

Let’s imagine you’re feeling pretty bummed after a bad day. As soon as you’re home, you shower, eat and log into Netflix.

Because you’re now in 202x and contextual analysis is in full swing, Netflix is able to suggest you the perfect pick-me-up movie!

At the moment, the recommendations of Netflix and many other streaming apps are based on your past viewing behavior. Once it is able to analyze context, its recommendations will improve even more.

What does this mean for the customer experience?

By using AI, businesses will not only be able to understand the context of different life events, they will also be able to understand emotions.

Artificial emotional intelligence, or Emotion AI, can be used to detect non-verbal cues, such as facial expressions, gestures, body language and tone of voice.

It will allow businesses to pick up on a customer’s current mood, informing how to respond to deliver the optimal experience.

But today’s consumers aren’t too keen on the idea. In their annual survey “Creepy or Cool”, RichRelevance asked consumers what they think is the creepiest technology. 58% say that “emotion detection technology that adapts your shopping experience to your mood” is off-putting.

All in all, the use of AI has become inevitable. It has become a key part of the success of any online business and will continue to be in the future. Customers want it and businesses need to get to grips with it.

Organizations who ignore it will see sales and engagement dwindle, and they’ll start to look unfashionable and far from modern.

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